Target tracking device and target tracking method

ABSTRACT

According to one embodiment, target tracking device includes track calculator and correction unit. Track calculator calculates a track of a target based on angular measurement of the target measured by a passive sensor. Correction unit sends correction data to the track calculator. Correction unit calculates correction data based on state vector of target input from an external device. Track calculator calculates track for each of a plurality of motion models based on angular measurement and correction data. Track calculator calculates the track of the target based on the track for each of the motion models. Track calculator calculates the track of the target by weighted sum of all tracks for the motion models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromprior Japanese Patent Application No. 2011-040363, filed Feb. 25, 2011,the entire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a target trackingdevice and a target tracking method which track a target using a passivesensor that measures the angle of the target to obtain angularmeasurement.

BACKGROUND

FIG. 8 is a block diagram showing an example of a conventional targettracking device. This target tracking device comprises a passive sensor1, a tracking processor 2, and a control unit 3. The passive sensor 1measures the angle of a radio wave, infrared rays, a sound wave, or thelike radiated (and reradiated) from a target. That is, the passivesensor 1 measures the angle of the target, thereby obtaining angularmeasurement. The passive sensor 1 sends the angular measurement to thetracking processor 2.

The tracking processor 2 calculates a predicted state and an updatedstate based on the angular measurement from the passive sensor 1. Thetracking processor 2 sends the predicted state and the updated state tothe control unit 3 as the target track. Based on the target track fromthe tracking processor 2, the control unit 3 generates a control signalto control the posture and the like of the passive sensor 1, and sendsthe signal to the passive sensor 1.

FIG. 9 is a flowchart illustrating the procedure executed by theconventional target tracking device.

When target tracking processing starts, angular measurement is input tothe tracking processor 2 (ST101). That is, the passive sensor 1 measuresthe target based on the control signal from the control unit 3, andsends the angular measurement of the target obtained by the measurementto the tracking processor 2. The tracking processor 2 acquires theangular measurement sent from the passive sensor 1.

Prediction processing is executed (ST102). That is, the trackingprocessor 2 calculates the predicted state of the target and itscovariance matrix based on the updated state of the target and itscovariance matrix calculated in step ST103 of the preceding measurement.

Update processing is then executed (ST103). That is, based on theangular measurement of the target from the passive sensor 1 and thepredicted state of the target and its covariance matrix calculated instep ST102, the tracking processor 2 calculates the updated state of thetarget and its covariance matrix and sends them to the control unit 3 asthe target track.

Control processing is executed (ST104). That is, based on the targettrack from the tracking processor 2, the control unit 3 generates acontrol signal to control the posture and the like of the passive sensor1, and sends the signal to the passive sensor 1. The processing of stepsST101 to ST105 is continued until the end.

The processing contents of the tracking processor 2 will be described indetail. The motion model of the target is defined in the following way.Note that “bar x” will be expressed as “x(-)” hereinafter.

$\begin{matrix}{{\overset{\_}{x}}_{k + 1} = {{F_{k + 1}{\overset{\_}{x}}_{k}} + {G_{k + 1}W_{k}}}} & (1) \\{{\overset{\_}{x}}_{k} = \left\lbrack \begin{matrix}a_{k} & e_{k} & {\overset{.}{a}}_{k} & \left. {\overset{.}{e}}_{k} \right\rbrack^{T}\end{matrix} \right.} & (2) \\{F_{k + 1} = \begin{bmatrix}I_{2} & {\left( {t_{k + 1} - t_{k}} \right) \cdot I_{2}} \\O_{2} & I_{2}\end{bmatrix}} & (3) \\{G_{k + 1} = \begin{bmatrix}{\frac{\left( {t_{k + 1} - t_{k}} \right)^{2}}{2} \cdot I_{2}} \\{\left( {t_{k + 1} - t_{k}} \right) \cdot I_{2}}\end{bmatrix}} & (4) \\{Q_{k} = {\frac{1}{r_{k}}\begin{bmatrix}\left( \sigma_{k}^{h} \right)^{2} & 0 \\0 & \left( \sigma_{k}^{v} \right)^{2}\end{bmatrix}}} & (5)\end{matrix}$where x(-)_(k) is a state vector including an azimuth a_(k), anelevation e_(k), and their velocity components at an measurement timet_(k), F_(k+1) and G_(k+1) are the transition matrix and the drivingmatrix from the measurement time t_(k) to an measurement time t_(k+1),respectively, w_(k) is the process noise vector at the measurement timet_(k) for an average 0 and a covariance matrix Q_(k), σ^(h) _(k) andσ^(v) _(k) are the standard deviations of the horizontal and verticalplanes of process noise at the measurement time t_(k), respectively,r_(k) is the distance from the passive sensor 1 to the target at themeasurement time t_(k), A^(T) is the transposition of a vector or matrixA, I_(n) is an n×n identity matrix, and O_(n) is an n×n zero matrix.

The measurement model of the passive sensor 1 is defined by

$\begin{matrix}{y_{k} = {{H_{k}{\overset{\_}{x}}_{k}} + v_{k}}} & (6) \\{H_{k} = \begin{bmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0\end{bmatrix}} & (7) \\{R_{k} = \begin{bmatrix}\left( \sigma_{k}^{a} \right)^{2} & 0 \\0 & \left( \sigma_{k}^{e} \right)^{2}\end{bmatrix}} & (8)\end{matrix}$where y_(k) is the measurement vector of the passive sensor 1 at themeasurement time t_(k), H_(k) is the measurement matrix of the passivesensor 1 at the measurement time t_(k), v_(k) is the measurement noisevector of the passive sensor 1 at the measurement time t_(k) for anaverage 0 and a covariance matrix R_(k), and σ^(a) _(k) and σ^(e) _(k)are the standard deviations of the azimuth and elevation of measurementnoise at the measurement time t_(k), respectively.

In step ST101, the angular measurement from the passive sensor 1 isinput as the measurement vector y_(k).

In step ST102, prediction processing represented by the followingequations is executed using the result of update processing of thepreceding measurement. Note that “hat x” will be expressed as “x(^)”hereinafter.

$\begin{matrix}{{\hat{x}}_{k|{k - 1}} = {F_{k}{\hat{x}}_{{k - 1}|{k - 1}}}} & (9) \\{P_{k|{k - 1}} = {{F_{k}{P_{{k - 1}|{k - 1}}\left( F_{k} \right)}^{T}} + {G_{k}{Q_{k - 1}\left( G_{k} \right)}^{T}}}} & (10) \\{Q_{k - 1} = {\frac{1}{r_{preset}}\begin{bmatrix}\left( \sigma_{k - 1}^{h} \right)^{2} & 0 \\0 & \left( \sigma_{k - 1}^{v} \right)^{2}\end{bmatrix}}} & (11)\end{matrix}$where x(^)_(k|k−1) and P_(k|k−1) are the predicted state vector and thepredicted error covariance matrix at the measurement time t_(k),respectively, and x(^)_(k−1|k−1) and P_(k−1|k−1) are the updated statevector and the updated error covariance matrix at an measurement timet_(k−1), respectively. Since a true value r_(k−1) of the target distancecannot be known, a preset target distance r_(preset) is used whencalculating a process noise covariance matrix Q_(k−1).

In step ST103, update processing represented by the following equationsis executed using the measurement vector from the passive sensor 1 andthe result of prediction processing. Note that “tilde y” will beexpressed as “y(˜)” hereinafter.{tilde over (y)} _(k) =y _(k) −H _(k) {circumflex over (x)}_(k|k−1)  (12)S _(k) =H _(k) P _(k|k−1)(H _(k))^(T) +R _(k)  (13)K _(k) =P _(k|k−1)(H _(k))^(T)(S _(k))⁻¹  (14){circumflex over (x)} _(k|k) ={circumflex over (x)} _(k|k−1) +K _(k){tilde over (y)} _(k)  (15)P _(k|k)=(I ₄ −K _(k) H _(k))P _(k|k−1)  (16)where y(˜)_(k) is the residual vector of the passive sensor 1 at themeasurement time t_(k), S_(k) is the residual covariance matrix of thepassive sensor 1 at the measurement time t_(k), K_(k) is the Kalman gainmatrix of the passive sensor 1 at the measurement time t_(k), x(^)_(k|k)and P_(k|k) are the updated state vector and the updated errorcovariance matrix at the measurement time t_(k), respectively, and A⁻¹is the inverse matrix of the matrix A.

As described above, in the tracking processing of the passive sensor 1,the process noise covariance matrix Q_(k−1) includes an error becausethe distance data from the passive sensor 1 to the target is notavailable. It is consequently difficult to calculate the optimum valueof the filter gain (Kalman gain matrix) that is indirectly calculatedfrom the process noise covariance matrix and used to calculate thetrack. Hence, the track error of the target becomes large.

Even for a target that performs constant velocity (non-maneuver) on theorthogonal coordinate system, an angular acceleration and thedifferential component of the angular acceleration are generated on thepolar coordinate system. Since it is difficult to estimate the componentfrom angular measurement and reflect it on the process noise covariancematrix Q_(k−1), the track error becomes large.

As a technique of improving the tracking performance for both a targetthat performs non-maneuver and a target that performs maneuver, anInteracting Multiple Model (IMM) filter is known, which operates aplurality of motion models in parallel. However, since many motionmodels are generally defined as a motion on a three-dimensionalorthogonal coordinate system, it is difficult to apply the technique tothe tracking processor 2 that estimates the target track on atwo-dimensional polar coordinate system or the like.

As described above, in the target tracking device using a passivesensor, the distance data from the passive sensor 1 to the target is notobtained in general. It is therefore difficult to calculate the optimumvalue of the filter gain to be used to calculate the track, and thetrack error becomes large.

Since the distance data to the target cannot be obtained, the target istracked on a local coordinate system about the passive sensor. However,when, for example, a polar coordinate system is used as the localcoordinate system, an angular acceleration and the differentialcomponent of the angular acceleration are generated on the polarcoordinate system even if the target performs constant velocity(non-maneuver) on the orthogonal coordinate system.

When the filter gain is increased to cope with the above-describedproblem, the random component of the track error becomes large. When thefilter gain is decreased to make the random component of the track errorsmaller, the bias component of the track error becomes large. At anyrate, it is difficult to improve the tracking performance.

When a technique assuming a single motion model is optimized for atarget that performs non-maneuver, tracking performance for a targetthat performs maneuver degrades. Similarly, when the technique isoptimized for a target that performs maneuver, tracking performance fora target that performs non-maneuver degrades. That is, it is difficultfor the existing technique to improve the tracking performance for botha target that performs non-maneuver and a target that performs maneuver.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary block diagram of a target tracking deviceaccording to the first embodiment;

FIG. 2 is a flowchart illustrating the procedure of target trackingprocessing performed by the target tracking device according to thefirst embodiment;

FIG. 3 is a flowchart illustrating the procedure executed by a targettracking device according to the second embodiment;

FIG. 4 is a flowchart illustrating the procedure executed by a targettracking device according to the third embodiment;

FIG. 5 is a flowchart illustrating the procedure executed by a targettracking device according to the fourth embodiment;

FIG. 6 is a flowchart illustrating the procedure executed by a targettracking device according to the fifth embodiment;

FIGS. 7A, 7B, and 7C shows exemplary block diagrams of target trackingdevices according to modifications of the first to fifth embodiments;

FIG. 8 is a block diagram showing an example a conventional targettracking device;

FIG. 9 is a flowchart illustrating the procedure executed by theconventional target tracking device; and

FIG. 10 is an exemplary block diagram of a target tracking deviceaccording to another embodiment.

DETAILED DESCRIPTION

In general, according to one embodiment, a target tracking deviceincludes a track calculation unit and a correction unit. The trackcalculation unit calculates a track of a target based on angularmeasurement of the target measured by a passive sensor. The correctionunit sends correction data to the track calculation unit. The correctionunit calculates the correction data based on a state vector of thetarget. The track calculation unit calculates track for each of aplurality of motion models based on the angular measurement and thecorrection data. The track calculation unit calculates the track of thetarget based on the track for each of the motion models.

The target tracking device according to an embodiment will now bedescribed in detail with reference to the accompanying drawings. Thesame reference numerals as in FIG. 8 denote the common blocks in thefollowing explanation. The common processing steps are indicated by thesame step numbers in the flowcharts including FIG. 9.

First Embodiment

FIG. 1 shows an exemplary block diagram of a target tracking deviceaccording to the first embodiment. The target tracking device shown inFIG. 1 comprises a passive sensor 1, a tracking processor 2 a, a controlunit 3, a communication unit 4, and a correction data calculation unit 5a.

The passive sensor 1 measures the angle of a target and sends theobtained angular measurement to the tracking processor 2 a. Thecommunication unit 4 sends the state vector of the target input from anexternal device to the correction data calculation unit 5 a. Thecorrection data calculation unit 5 a calculates correction data on alocal coordinate system (polar coordinate system) about the passivesensor 1 based on the state vector of the target sent via thecommunication unit 4. The correction data calculation unit 5 acalculates the target distance (the distance from the passive sensor 1to the target) and sends it to the tracking processor 2 a.

Note that a radar device, passive sensors performing stereo view or thelike is usable as the external devices.

Based on the angular measurement from the passive sensor 1 and thecorrection data (target distance) from the correction data calculationunit 5 a, the tracking processor 2 a calculates a predicted state foreach of a plurality of motion models and an updated state for each ofthe plurality of motion models. The tracking processor 2 a alsocalculates an updated state by weighted sum of the updated states forthe plurality of motion models, and sends the updated state to thecontrol unit 3 as the target track. Based on the target track from thetracking processor 2 a, the control unit 3 generates a control signal tocontrol the posture and the like of the passive sensor 1 and sends thesignal to the passive sensor 1. The functions of the target trackingdevice according to the first embodiment will be described next.

FIG. 2 is a flowchart illustrating the procedure executed by the targettracking device according to the first embodiment. When trackingprocessing starts, angular measurement is input (ST101). That is, thepassive sensor 1 measures the target based on the control signal fromthe control unit 3, and sends the angular measurement of the target tothe tracking processor 2 a. The tracking processor 2 a acquires theangular measurement sent from the passive sensor 1.

Correction data calculation processing (target distance) is executed(ST111). That is, the correction data calculation unit 5 a calculatesdistance data from the passive sensor 1 to the target based on the statevector of the target input from the external device. The correction datacalculation unit 5 a sends the distance data to the tracking processor 2a as the correction data (target distance). In the followingexplanation, the correction data is used to perform correction whencalculating the target track based on the angular measurement from thepassive sensor 1.

Covariance calculation processing is executed (ST112). That is, thetracking processor 2 a calculates a process noise covariance matrix foreach of the plurality of motion models based on the correction data(target distance) from the correction data calculation unit 5 a.

Prediction processing is executed (ST113). That is, the predicted stateof the target and its covariance matrix are calculated for each of theplurality of motion models based on the updated states of the target andtheir covariance matrices for the plurality of motion models and theprocess noise covariance matrices for the plurality of motion models.

The updated states of the target and their covariance matrices for theplurality of motion models are calculated in step ST114 of the precedingmeasurement. The process noise covariance matrices for the plurality ofmotion models are calculated in step ST112.

Update processing is then executed (ST114). That is, based on theangular measurement of the target from the passive sensor 1 and thepredicted states of the target and their covariance matrices for theplurality of motion models, the tracking processor 2 a calculates theupdated state of the target and its covariance matrix for each of theplurality of motion models.

The tracking processor 2 a also calculates the updated state of thetarget and its covariance matrix by weighted sum of the updated statesof the target and their covariance matrices for the plurality of motionmodels. The calculated updated state and its covariance matrix are sentto the control unit 3 as the target track. The predicted states of thetarget and their covariance matrices for the plurality of motion modelsare calculated in step ST113.

Control processing is executed (ST104). That is, based on the targettrack from the tracking processor 2 a, the control unit 3 generates acontrol signal to control the posture and the like of the passive sensor1, and sends the signal to the passive sensor 1. It is then checkedwhether to end (ST105). Upon determining in step ST105 not to end, theprocess returns to step ST101 to repeat the above-described processing.On the other hand, upon determining in step ST105 to end, the targettracking processing ends.

The processing contents of the correction data calculation unit 5 a andthe tracking processor 2 a will be described next in detail. The motionmodel of the target is defined by

$\begin{matrix}{x_{k + 1}^{i} = {{F_{k + 1}x_{k}^{i}} + {G_{k + 1}w_{k}^{i}}}} & (17) \\{x_{k}^{i} = \left\lbrack \begin{matrix}a_{k}^{i} & e_{k}^{i} & {\overset{.}{a}}_{k}^{i} & \left. {\overset{.}{e}}_{k}^{i} \right\rbrack^{T}\end{matrix} \right.} & (18) \\{F_{k + 1} = \begin{bmatrix}I_{2} & {\left( {t_{k + 1} - t_{k}} \right) \cdot I_{2}} \\O_{2} & I_{2}\end{bmatrix}} & (19) \\{G_{k + 1} = \begin{bmatrix}{\frac{\left( {t_{k + 1} - t_{k}} \right)^{2}}{2} \cdot I_{2}} \\{\left( {t_{k + 1} - t_{k}} \right) \cdot I_{2}}\end{bmatrix}} & (20) \\{Q_{k}^{i} = {\frac{1}{r_{k}}\begin{bmatrix}\left( \sigma_{k}^{h,i} \right)^{2} & 0 \\0 & \left( \sigma_{k}^{v,i} \right)^{2}\end{bmatrix}}} & (21)\end{matrix}$where x^(i) _(k) is a state vector corresponding to the ith motion modeland including an azimuth a^(i) _(k), an elevation e^(i) _(k), and theirvelocity components at an measurement time t_(k), F_(k+1) and G_(k+1)are the transition matrix and the driving matrix from the measurementtime t_(k) to an measurement time t_(k+1), respectively, w^(i) _(k) isthe process noise vector corresponding to the ith motion model at themeasurement time t_(k) for an average 0 and a covariance matrix Q^(i)_(k), σ^(h,i) _(k) and σ^(v,i) _(k) are the standard deviations of thehorizontal and vertical planes of process noise corresponding to the ithmotion model at the measurement time t_(k), respectively, r_(k) is thedistance from the passive sensor 1 to the target at the measurement timet_(k), A^(T) is the transposition of a vector or matrix A, I_(n) is ann×n identity matrix, and O_(n) is an n×n zero matrix.

The measurement model of the passive sensor 1 is defined by

$\begin{matrix}{y_{k} = {{H_{k}x_{k}^{t}} + v_{k}}} & (22) \\{H_{k} = \begin{bmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0\end{bmatrix}} & (23) \\{R_{k} = \begin{bmatrix}\left( \sigma_{k}^{a} \right)^{2} & 0 \\0 & \left( \sigma_{k}^{e} \right)^{2}\end{bmatrix}} & (24)\end{matrix}$where y_(k) is the measurement vector of the passive sensor 1 at themeasurement time t_(k), x^(t) _(k) is the true state vector at themeasurement time t_(k), H_(k) is the measurement matrix of the passivesensor 1 at the measurement time t_(k), v_(k) is the measurement noisevector of the passive sensor 1 at the measurement time t_(k) for anaverage 0 and a covariance matrix R_(k), and σ^(a) _(k) and σ^(e) _(k)are the standard deviations of the azimuth and elevation of measurementnoise at the measurement time t_(k), respectively.

In step ST101, the angular measurement from the passive sensor 1 isinput to the tracking processor 2 a as the measurement vector y_(k).

In step ST111, the correction data calculation unit 5 a calculates atarget distance r(^)_(k−1) as correction data based on the state vectorof the target input from the external device.{circumflex over (r)} _(k−1)=√{square root over ((x _(k−1) −x ₀)²+(y_(k−1) −y ₀)²+(z _(k−1) −z ₀)²)}{square root over ((x _(k−1) −x ₀)²+(y_(k−1) −y ₀)²+(z _(k−1) −z ₀)²)}{square root over ((x _(k−1) −x ₀)²+(y_(k−1) −y ₀)²+(z _(k−1) −z ₀)²)}  (25)where x_(k−1), y_(k−1), and z_(k−1) are x-, y-, and z-components at theposition of the state vector of the target at an measurement timet_(k−1), and x0, y0, and z0 are x-, y-, and z-components at the positionof the passive sensor 1.

Note that a radar device or the like is usable as the external devices.The target distance may be directly measured using a distancemeasurement device such as a laser ranging device arranged at almost thesame position as the passive sensor 1.

In step ST112, the tracking processor 2 a calculates a process noisecovariance matrix Q^(i) _(k−1) corresponding to the ith motion modelbased on the correction data (target distance) from the correction datacalculation unit 5 a.

$\begin{matrix}{Q_{k - 1}^{i} = {\frac{1}{{\hat{r}}_{k - 1}}\begin{bmatrix}\left( \sigma_{k - 1}^{h,i} \right)^{2} & 0 \\0 & \left( \sigma_{k - 1}^{v,i} \right)^{2}\end{bmatrix}}} & (26)\end{matrix}$

In step ST113, the tracking processor 2 a executes, using the result ofupdate processing of the preceding measurement and the process noisecovariance matrix Q^(i) _(k−1) corresponding to the ith motion modelobtained by equation (26) in step ST112, prediction processingrepresented by{circumflex over (x)} _(k|k−1) ^(i) =F _(k) {circumflex over (x)}_(k−1|k−1) ^(i)  (27)P _(k|k−1) ^(i) =F _(k) P _(k−1|k−1) ^(i)(F _(k))^(T) +G _(k) Q _(k−1)^(i)(G _(k))^(T)  (28)where x(^)^(i) _(k|k−1) and p^(i) _(k|k−1) are the predicted statevector and the predicted error covariance matrix corresponding to theith motion model at the measurement time t_(k), respectively, andx(^)^(i) _(k−1|k−1) and p^(i) _(k−1|k−1) are the updated state vectorand the updated error covariance matrix corresponding to the ith motionmodel at the measurement time t_(k−1), respectively.

In step ST114, the tracking processor 2 a executes, using themeasurement vector from the passive sensor 1 and the result ofprediction processing, update processing represented by

$\begin{matrix}{{\overset{\sim}{y}}_{k}^{i} = {y_{k} - {H_{k}{\hat{x}}_{k|{k - 1}}^{i}}}} & (29) \\{S_{k}^{i} = {{H_{k}{P_{k|{k - 1}}^{i}\left( H_{k} \right)}^{T}} + R_{k}}} & (30) \\{K_{k}^{i} = {{P_{k|{k - 1}}^{i}\left( H_{k} \right)}^{T}\left( S_{k}^{t} \right)^{- 1}}} & (31) \\{{\hat{x}}_{k|k}^{i} = {{\hat{x}}_{k|{k - 1}}^{i} + {K_{k}^{i}{\overset{\sim}{y}}_{k}^{i}}}} & (32) \\{P_{k|k}^{i} = {\left( {I_{4} - {K_{k}^{i}H_{k}}} \right)P_{k|{k - 1}}^{i}}} & (33) \\{p_{k}^{i} = \frac{l_{k}^{i}p_{k - 1}^{i}}{\sum\limits_{j = 1}^{n}\;{l_{k}^{j}p_{k - 1}^{j}}}} & (34) \\{{\hat{x}}_{k|k} = {\sum\limits_{i = 1}^{n}\;{p_{k}^{i}{\hat{x}}_{k|k}^{i}}}} & (35) \\{P_{k|k} = {\sum\limits_{i = 1}^{n}\;{p_{k}^{i}\left( {P_{k|k}^{i} + {\left( {{\hat{x}}_{k|k}^{i} - {\hat{x}}_{k|k}} \right)\left( {{\hat{x}}_{k|k}^{i} - {\hat{x}}_{k|k}} \right)^{T}}} \right)}}} & (36)\end{matrix}$where y(˜)^(i) _(k) is the residual vector of the passive sensor 1corresponding to the ith motion model at the measurement time t_(k),S^(i) _(k) is the residual covariance matrix of the passive sensor 1corresponding to the ith motion model at the measurement time t_(k),K^(i) _(k) is the Kalman gain matrix of the passive sensor 1corresponding to the ith motion model at the measurement time t_(k),x(^)^(i) _(k|k) and P^(i) _(k|k) are the updated state vector and theupdated error covariance matrix corresponding to the ith motion model atthe measurement time t_(k), respectively, p^(i) _(k) is the motion modelprobability corresponding to the ith motion model at the measurementtime t_(k), l^(i) _(k) us the motion model likelihood corresponding tothe ith motion model at the measurement time t_(k), x(^)_(k|k) andP_(k|k) are the updated state vector and updated error covariance matrixat the measurement time t_(k) which are obtained by weighted sum of theplurality of motion models, and A⁻¹ is the inverse matrix of the matrixA.

As described above, according to the target tracking device of the firstembodiment, the correction data calculation unit 5 a calculatescorrection data (target distance) based on the state vector of thetarget input from the external device. The tracking processor 2 acalculates the process noise covariance matrix for each of the pluralityof motion models based on the correction data (target distance) from thecorrection data calculation unit 5 a. The tracking processor 2 a uses,for track calculation, a filter gain (Kalman gain matrix) indirectlycalculated from the value of the process noise covariance matrix. It istherefore possible to reduce the track error (random component) of atarget that performs non-maneuver and the track error (bias component)of a target that performs maneuver.

Note that although in the above description, a constant velocity modelis used as the motion model of the target, the above-describedprocessing is also applicable to another motion model such as a constantacceleration model. The tracking processor 2 a may execute IMM filterprocessing or the like.

In the above-described example, the state vector of the target of thetracking processor 2 a is represented on the polar coordinate system.Instead, the position (the horizontal and vertical coordinates and thevelocity) of the target on a camera image or the like may be used.

In the target tracking device according to the first embodiment, thecorrection data calculation unit 5 a calculates the target distance asthe correction data. The tracking processor 2 a calculates the processnoise covariance matrix for each of the plurality of motion models basedon the correction data (target distance). Instead, the correction datacalculation unit 5 a may calculate the process noise covariance matrixfor each of the plurality of motion models as the correction data. Thetracking processor 2 a may calculate the filter gain based on thecorrection data (process noise covariance matrix for each of theplurality of motion models).

Second Embodiment

In a target tracking device according to the second embodiment, thefunctions of the correction data calculation unit 5 a and the trackingprocessor 2 a shown in FIG. 1 are slightly different from those of thefirst embodiment. In the second embodiment, reference numeral 5 bdenotes a correction data calculation unit; and 2 b, a trackingprocessor. Only parts different from the target tracking deviceaccording to the first embodiment will be explained below.

The correction data calculation unit 5 b calculates correction data on alocal coordinate system (polar coordinate system) about a passive sensor1 based on the state vector of a target sent from a communication unit4. In the second embodiment, the target distance (distance from thepassive sensor 1 to the target) and the process noise covariance matrixof the target are used as the correction data.

Based on angular measurement from the passive sensor 1 and thecorrection data (target distance and process noise covariance matrix)from the correction data calculation unit 5 b, the tracking processor 2b calculates a predicted state for each of a plurality of motion modelsand an updated state for each of the plurality of motion models. Thetracking processor 2 b also calculates an updated state by weighted sumof the updated states for the plurality of motion models, and sends theupdated state to a control unit 3 as the target track. The functions ofthe target tracking device according to the second embodiment will bedescribed next.

FIG. 3 is a flowchart illustrating the procedure executed by the targettracking device according to the second embodiment. When trackingprocessing starts, angular measurement is input (ST101). That is, thepassive sensor 1 measures the target based on a control signal from thecontrol unit 3, and sends the angular measurement of the target to thetracking processor 2 b. The tracking processor 2 b acquires the angularmeasurement sent from the passive sensor 1.

Correction data calculation processing (target distance) is executed(ST111). That is, the correction data calculation unit 5 b calculatesdistance data from the passive sensor 1 to the target based on the statevector of the target input from the external device. The correction datacalculation unit 5 b sends the distance data to the tracking processor 2b as the correction data (target distance).

Correction data calculation processing (covariance) is executed (ST121).That is, the correction data calculation unit 5 b calculates the processnoise covariance matrix of the target based on the state vector of thetarget input from the external device. The process noise covariancematrix is sent to the tracking processor 2 b as the correction data.

Note that as the external device, a device having a function ofestimating the process noise covariance matrix of the target isapplicable. Examples of a device of this type are a radar deviceincluding an adaptive Kalman filter and a radar device including an IMMfilter.

Covariance calculation processing is executed (ST112). That is, thetracking processor 2 b calculates a process noise covariance matrix foreach of the plurality of motion models based on the correction data(target distance) from the correction data calculation unit 5 b.

Motion model probability calculation processing is then executed(ST122). That is, the tracking processor 2 b calculates a motion modelprobability for each of the plurality of motion models based on thecorrection data (process noise covariance matrix) from the correctiondata calculation unit 5 b.

Prediction processing is executed (ST113). That is, the trackingprocessor 2 b calculates the predicted state of the target and itscovariance matrix for each of the plurality of motion models based onthe updated states of the target and their covariance matrices for theplurality of motion models and the process noise covariance matrices forthe plurality of motion models. The updated states of the target andtheir covariance matrices for the plurality of motion models arecalculated in step ST114 of the preceding measurement. The process noisecovariance matrices for the plurality of motion models are calculated instep ST112.

Update processing is then executed (ST114). That is, based on theangular measurement of the target from the passive sensor 1 and thepredicted states of the target and their covariance matrices for theplurality of motion models, the tracking processor 2 b calculates theupdated state of the target and its covariance matrix for each of theplurality of motion models. The predicted states of the target and theircovariance matrices for the plurality of motion models are calculated instep ST113.

The tracking processor 2 b also calculates the updated state of thetarget and its covariance matrix by weighted sum of the updated statesof the target and their covariance matrix. These values are sent to thecontrol unit 3 as the target track.

Control processing is executed (ST104). That is, based on the targettrack from the tracking processor 2 b, the control unit 3 generates acontrol signal to control the posture and the like of the passive sensor1. The control signal is sent to the passive sensor 1. The processing ofsteps ST101 to ST105 is continued until the end.

The processing contents of the correction data calculation unit 5 b andthe tracking processor 2 b will be described next in detail withreference to FIG. 3. The motion model of the target and the measurementmodel of the passive sensor 1 are the same as in the first embodiment.Referring to FIG. 3, the processing contents of steps ST101, ST111,ST112, ST113, ST104, and ST105 are the same as in the first embodiment.

In step ST121, based on the state vector of the target (including aprocess noise covariance matrix Q^(a) _(k−1)) input from the externaldevice, a process noise covariance matrix Q_(k−1) viewed from thepassive sensor 1 is calculated as correction data by

$\begin{matrix}{Q_{k - 1} = {{T_{k - 1}{Q_{k - 1}^{a}\left( T_{k - 1} \right)}^{T}} = \begin{bmatrix}V_{k - 1}^{aa} & V_{k - 1}^{ae} \\V_{k - 1}^{ae} & V_{k - 1}^{ee}\end{bmatrix}}} & (37)\end{matrix}$

When the process noise covariance matrix Q^(a) _(k−1) input from theexternal device is given by

$\begin{matrix}{Q_{k - 1}^{a} = \begin{bmatrix}V_{k - 1}^{xx} & V_{k - 1}^{xy} & V_{k - 1}^{xz} \\V_{k - 1}^{xy} & V_{k - 1}^{yy} & V_{k - 1}^{yz} \\V_{k - 1}^{xz} & V_{k - 1}^{yz} & V_{k - 1}^{zz}\end{bmatrix}} & (38)\end{matrix}$a transformation matrix T_(k−1) is given by

$\begin{matrix}{T_{k - 1} = {\frac{\partial\left( {a_{k - 1},e_{k - 1}} \right)}{\partial\left( {x_{k - 1},y_{k - 1},z_{k - 1}} \right)} = \begin{bmatrix}\frac{\partial a_{k - 1}}{\partial x_{k - 1}} & \frac{\partial a_{k - 1}}{\partial y_{k - 1}} & \frac{\partial a_{k - 1}}{\partial z_{k - 1}} \\\frac{\partial e_{k - 1}}{\partial x_{k - 1}} & \frac{\partial e_{k - 1}}{\partial y_{k - 1}} & \frac{\partial e_{k - 1}}{\partial z_{k - 1}}\end{bmatrix}}} & (39)\end{matrix}$

Note that (x_(k−1), y_(k−1), z_(k−1)) and (a_(k−1), e_(k−1)) are valuesobtained by converting the state vector of the target input from theexternal device into the position vector (orthogonal coordinate systemand polar coordinate system) of the target viewed from the passivesensor 1.

In step ST122, using the process noise covariance matrix Q_(k−1) and aprocess noise covariance matrix Q^(i) _(k−1) corresponding to the ithmotion model, the tracking processor 2 b calculates a motion modelprobability p^(i) _(k−1) corresponding to the ith motion model andsatisfying

$\begin{matrix}\left. \begin{matrix}{Q_{k - 1} \approx {\sum\limits_{i = 1}^{n}\;{p_{k - 1}^{i}Q_{k - 1}^{i}}}} \\{{\sum\limits_{i = 1}^{n}\; p_{k - 1}^{i}} = 1}\end{matrix} \right\} & (40)\end{matrix}$

The process noise covariance matrix Q_(k−1) is calculated in step ST121.The process noise covariance matrix Q^(i) _(k−1) corresponding to theith motion model is calculated in step ST112.

Note that if the defined (set) motion model does not strictly apply tothe actual motion model, the process noise covariance matrix Q^(i)_(k−1) corresponding to the ith motion model can be corrected inconsideration of the error component in modeling.

In step ST114, update processing is executed using the motion modelprobability p^(i) _(k−1) corresponding to the ith motion model, which iscalculated by equation (40) in step ST122 in place of equation (34) ofupdate processing of the preceding measurement (step ST114).

As described above, according to the target tracking device of thesecond embodiment, the correction data calculation unit 5 b calculatescorrection data (target distance and process noise covariance matrix)based on the state vector of the target input from the external device.The tracking processor 2 b uses, for track calculation, a filter gain(Kalman gain matrix) indirectly calculated from the correction data(target distance) sent from the correction data calculation unit 5 b foreach of the plurality of motion models.

It is therefore possible to reduce the track error (random component) ofa target that performs non-maneuver and the track error (bias component)of a target that performs maneuver.

Additionally, the tracking processor 2 b uses, for track calculation,the motion model probability indirectly calculated from the correctiondata (process noise covariance matrix) for each of the plurality ofmotion models. This allows to reduce the track error at initial trackingstage when the number of times of measurement is small. Also, thisallows to reduce the track error for both the target that performsnon-maneuver and the target that performs maneuver even when the motionmodel of the target has changed after the preceding measurement.

Third Embodiment

In a target tracking device according to the third embodiment, thefunctions of the correction data calculation unit 5 a and the trackingprocessor 2 a shown in FIG. 1 are slightly different from those of thefirst embodiment. In the third embodiment, reference numeral 5 c denotesa correction data calculation unit; and 2 c, a tracking processor. Onlyparts different from the target tracking device according to the firstembodiment will be explained below.

The correction data calculation unit 5 c calculates correction data on alocal coordinate system (polar coordinate system) about a passive sensor1 based on the state vector of a target sent via a communication unit 4.In the third embodiment, the angular acceleration of the target, whichis generated when the constant velocity model on the polar coordinatesystem defined (set) by equation (17) does not strictly apply to theconstant velocity model on the orthogonal coordinate system, is regardedas a control input vector. The correction data is sent to the trackingprocessor 2 c.

Based on angular measurement from the passive sensor 1 and thecorrection data (control input vector) from the correction datacalculation unit 5 c, the tracking processor 2 c calculates a predictedstate for each of a plurality of motion models and an updated state foreach of the plurality of motion models. The tracking processor 2 c alsocalculates an updated state by weighted sum of the updated states forthe plurality of motion models, and sends the updated state to a controlunit 3 as the target track. The functions of the target tracking deviceaccording to the third embodiment will be described next.

FIG. 4 is a flowchart illustrating the procedure executed by the targettracking device according to the third embodiment. Note that onlyprocessing procedures different from those of the first or secondembodiment will be explained below.

When tracking processing starts, angular measurement is input (ST101).That is, the passive sensor 1 measures the target based on a controlsignal from the control unit 3, and sends the angular measurement of thetarget to the tracking processor 2 c. The tracking processor 2 cacquires the angular measurement sent from the passive sensor 1.

Correction data calculation processing (control input) is executed(ST131). That is, the correction data calculation unit 5 c calculatesthe control input vector of the target based on the state vector of thetarget input from the external device. The correction data calculationunit 5 c sends the control input vector to the tracking processor 2 c asthe correction data.

Prediction processing is executed (ST132). That is, the trackingprocessor 2 c calculates the predicted state of the target and itscovariance matrix for each of the plurality of motion models based onthe updated states of the target and their covariance matrices for theplurality of motion models and the correction data (control inputvector) from the correction data calculation unit 5 c. The updatedstates of the target and their covariance matrices for the plurality ofmotion models are calculated in step ST114 of the preceding measurement.

Update processing is then executed (ST114). That is, based on theangular measurement of the target from the passive sensor 1 and thepredicted states of the target and their covariance matrices for theplurality of motion models, the tracking processor 2 c calculates theupdated state of the target and its covariance matrix for each of theplurality of motion models. The predicted states of the target and theircovariance matrices for the plurality of motion models are calculated instep ST132. The tracking processor 2 c also calculates the updated stateof the target and its covariance matrix by weighted sum of the updatedstates of the target and their covariance matrix for the plurality ofmotion models. The updated state of the target and its covariance matrixare sent to the control unit 3 as the target track.

Control processing is executed (ST104). That is, based on the targettrack from the tracking processor 2 c, the control unit 3 generates acontrol signal to control the posture and the like of the passive sensor1 and sends the signal to the passive sensor 1. The processing of stepsST101 to ST105 is continued until the end.

The processing contents of the correction data calculation unit 5 c andthe tracking processor 2 c will be described next in detail. The motionmodel of the target is defined by

$\begin{matrix}{x_{k + 1}^{i} = {{F_{k + 1}x_{k}^{i}} + {G_{k + 1}u_{k}} + {G_{k + 1}w_{k}^{i}}}} & (41) \\{x_{k}^{i} = \left\lbrack \begin{matrix}a_{k}^{i} & e_{k}^{i} & {\overset{.}{a}}_{k}^{i} & \left. {\overset{.}{e}}_{k}^{i} \right\rbrack^{T}\end{matrix} \right.} & (42) \\{u_{k} = \left\lbrack \begin{matrix}{\overset{¨}{a}}_{k} & \left. {\overset{¨}{e}}_{k} \right\rbrack^{T}\end{matrix} \right.} & (43) \\{F_{k + 1} = \begin{bmatrix}I_{2} & {\left( {t_{k + 1} - t_{k}} \right) \cdot I_{2}} \\O_{2} & I_{2}\end{bmatrix}} & (44) \\{G_{k + 1} = \begin{bmatrix}{\frac{\left( {t_{k + 1} - t_{k}} \right)^{2}}{2} \cdot I_{2}} \\{\left( {t_{k + 1} - t_{k}} \right) \cdot I_{2}}\end{bmatrix}} & (45) \\{Q_{k}^{i} = {\frac{1}{r_{k}}\begin{bmatrix}\left( \sigma_{k}^{h,i} \right)^{2} & 0 \\0 & \left( \sigma_{k}^{v,i} \right)^{2}\end{bmatrix}}} & (46)\end{matrix}$where x^(i) _(k) is a state vector corresponding to the ith motion modeland including an azimuth a^(i) _(k), an elevation e^(i) _(k), and theirvelocity components at an measurement time t_(k), u_(k) is the controlinput vector including the acceleration components of an azimuth a_(k)and an elevation e_(k) at the measurement time t_(k), F_(k+1) andG_(k+1) are the transition matrix and the driving matrix from themeasurement time t_(k) to an measurement time t_(k+1), respectively,w^(i) _(k) is the process noise vector corresponding to the ith motionmodel at the measurement time t_(k) for an average 0 and a covariancematrix Q^(i) _(k), σ^(h,i) _(k) and σ^(v,i) _(k) are the standarddeviations of the horizontal and vertical planes of process noisecorresponding to the ith motion model at the measurement time t_(k),respectively, r_(k) is the distance from the passive sensor 1 to thetarget at the measurement time t_(k), A^(T) is the transposition of avector or matrix A, I_(n) is an n×n identity matrix, and O_(n) is an n×nzero matrix.

The measurement model of the passive sensor 1 is the same as in thefirst embodiment. The processing contents of steps ST101, ST114, ST104,and ST105 are also the same as in the first embodiment.

In step ST131, based on the state vector of the target input from theexternal device, the correction data calculation unit 5 c calculates acontrol input vector (angular acceleration) u_(k−1) as correction databyu _(k−1) =[ä _(k−1) ë _(k−1)]^(T)  (47)

Note that the control input vector (angular acceleration) u_(k−1) can becalculated by

$\begin{matrix}\begin{matrix}{u_{k - 1} = {\frac{\partial\left( {{\overset{.}{a}}_{k - 1},{\overset{.}{e}}_{k - 1}} \right)}{\partial\left( {x_{k - 1},y_{k - 1},z_{k - 1}} \right)}\left\lbrack \begin{matrix}{\overset{.}{x}}_{k - 1} & {\overset{.}{y}}_{k - 1} & \left. {\overset{.}{z}}_{k - 1} \right\rbrack\end{matrix}^{T} \right.}} \\{= {\begin{bmatrix}\frac{\partial{\overset{.}{a}}_{k - 1}}{\partial x_{k - 1}} & \frac{\partial{\overset{.}{a}}_{k - 1}}{\partial y_{k - 1}} & \frac{\partial{\overset{.}{a}}_{k - 1}}{\partial z_{k - 1}} \\\frac{\partial{\overset{.}{e}}_{k - 1}}{\partial x_{k - 1}} & \frac{\partial{\overset{.}{e}}_{k - 1}}{\partial y_{k - 1}} & \frac{\partial{\overset{.}{e}}_{k - 1}}{\partial z_{k - 1}}\end{bmatrix}\begin{bmatrix}{\overset{.}{x}}_{k - 1} \\{\overset{.}{y}}_{k - 1} \\{\overset{.}{z}}_{k - 1}\end{bmatrix}}}\end{matrix} & (48)\end{matrix}$where (x_(k−1), y_(k−1), z_(k−1)), (x(•)_(k−1), y(•)_(k−1), z(•)_(k−1)),and (a(•)_(k−), e(•)_(k−1)) are vectors obtained bycoordinate-converting the state vector of the target input from theexternal device into the position vector, the velocity vector, and theangular velocity vector of the target viewed from the passive sensor 1.

In step ST132, using the result of update processing of the precedingmeasurement and the control input vector u_(k−1), the tracking processor2 c executes prediction processing represented by

$\begin{matrix}{{\hat{x}}_{k|{k - 1}}^{i} = {{F_{k}{\hat{x}}_{{k - 1}|{k - 1}}^{i}} + {G_{k}u_{k - 1}}}} & (49) \\{P_{k|{k - 1}}^{i} = {{F_{k}{P_{{k - 1}|{k - 1}}^{i}\left( F_{k} \right)}^{T}} + {G_{k}{Q_{k - 1}^{i}\left( G_{k} \right)}^{T}}}} & (50) \\{Q_{k - 1}^{i} = {\frac{1}{r_{preset}}\begin{bmatrix}\left( \sigma_{k - 1}^{h,i} \right)^{2} & 0 \\0 & \left( \sigma_{k - 1}^{v,i} \right)^{2}\end{bmatrix}}} & (51)\end{matrix}$

Since a true value r_(k−1) of the target distance cannot be known, apreset target distance r_(preset) is used when calculating a processnoise covariance matrix Q_(k−1) corresponding to the ith motion model.

As described above, according to the target tracking device of the thirdembodiment, the correction data calculation unit 5 c calculatescorrection data (control input vector) based on the state vector of thetarget input from the external device. The correction data is sent tothe tracking processor 2 c.

The tracking processor 2 c uses, for track calculation, a predictedstate indirectly calculated from the correction data (control inputvector) from the correction data calculation unit 5 c for each of theplurality of motion models. It is therefore possible to reduce the trackerror (bias component) for a target that performs non-maneuver and atarget that performs maneuver.

Note that although in the above description, a constant velocity modelis used as the motion model of the target, the above-describedprocessing is also applicable to another motion model such as a constantacceleration model. The control input vector u_(k−1) is assumed to beconstant independently of the motion model. Instead, the algorithm maybe configured such that the control input vector u_(k−1) takes adifferent value or differential order (for example, angular jerk (i.e.the rate of change of acceleration)) for each motion model. The trackingprocessor 2 c may execute IMM filter processing or the like.

Fourth Embodiment

In a target tracking device according to the fourth embodiment, thefunctions of the correction data calculation unit 5 a and the trackingprocessor 2 a shown in FIG. 1 are slightly different from those of thefirst embodiment. In the fourth embodiment, reference numeral 5 ddenotes a correction data calculation unit; and 2 d, a trackingprocessor. Only parts different from the target tracking deviceaccording to the first embodiment will be explained below.

The correction data calculation unit 5 d calculates correction data on alocal coordinate system (polar coordinate system) about a passive sensor1 based on the state vector of a target sent from a communication unit4. In the fourth embodiment, the target distance (distance from thepassive sensor 1 to the target) and the control input vector are used ascorrection data.

Based on angular measurement from the passive sensor 1 and thecorrection data (target distance and control input vector) from thecorrection data calculation unit 5 d, the tracking processor 2 dcalculates a predicted state for each of a plurality of motion modelsand updated state for each of the plurality of motion models. Thetracking processor 2 d also calculates an updated state by weighted sumof the updated states for the plurality of motion models, and sends theupdated state to a control unit 3 as the target track. The functions ofthe target tracking device according to the fourth embodiment will bedescribed next.

FIG. 5 is a flowchart illustrating the procedure executed by the targettracking device according to the fourth embodiment. Note that onlyprocessing procedures different from those of the first to thirdembodiments will be explained below.

When tracking processing starts, angular measurement is input (ST101).That is, the passive sensor 1 measures the target based on a controlsignal from the control unit 3, and sends the angular measurement of thetarget to the tracking processor 2 d. The tracking processor 2 dacquires the angular measurement sent from the passive sensor 1.

Correction data calculation processing (target distance) is executed(ST111). That is, the correction data calculation unit 5 d calculatesthe distance from the passive sensor 1 to the target based on the statevector of the target input from the external device, and sends thecalculated value to the tracking processor 2 d as the correction data(target distance).

Correction data calculation processing (control input) is executed(ST131). That is, the correction data calculation unit 5 d calculatesthe control input vector of the target based on the state vector of thetarget input from the external device, and sends the control inputvector to the tracking processor 2 d as the correction data (controlinput vector).

Covariance calculation processing is executed (ST112). That is, thetracking processor 2 d calculates a process noise covariance matrix foreach of the plurality of motion models based on the correction data(target distance) from the correction data calculation unit 5 d.

Prediction processing is executed (ST132). That is, the trackingprocessor 2 d calculates the predicted state of the target and itscovariance matrix for each of the plurality of motion models based onthe updated states of the target and their covariance matrices for theplurality of motion models and the correction data (target distance andcontrol input vector) from the correction data calculation unit 5 d. Theupdated states of the target and their covariance matrices for theplurality of motion models are calculated in step ST114 of the precedingmeasurement.

Update processing is then executed (ST114). That is, based on theangular measurement of the target from the passive sensor 1 and thepredicted states of the target and their covariance matrices for theplurality of motion models, the tracking processor 2 d calculates theupdated state of the target and its covariance matrix for each of theplurality of motion models. The tracking processor 2 d also calculatesthe updated state of the target and its covariance matrix by weightedsum of the updated states of the target and their covariance matrix forthe plurality of motion models. The updated state of the target and itscovariance matrix are sent to the control unit 3 as the target track.The predicted states of the target and their covariance matrices for theplurality of motion models are calculated in step ST132.

Control processing is executed (ST104). That is, based on the targettrack from the tracking processor 2 d, the control unit 3 generates acontrol signal to control the posture and the like of the passive sensor1 and sends the signal to the passive sensor 1. The processing of stepsST101 to ST105 is continued until the end.

The processing contents of the correction data calculation unit 5 d andthe tracking processor 2 d will be described next in detail. The motionmodel of the target is the same as in the third embodiment. Themeasurement model of the passive sensor 1 is the same as in the firstembodiment. The processing contents of steps ST101, ST111, ST112, ST114,ST104, and ST105 are the same as in the first embodiment. The processingcontents of step ST131 are the same as in the third embodiment.

In step ST132, the tracking processor 2 d executes prediction processingusing a process noise covariance matrix Q^(i) _(k−1) corresponding tothe ith motion model obtained by equation (26) in step ST112 in place ofequation (51).

As described above, according to the target tracking device of thefourth embodiment, the correction data calculation unit 5 d calculatescorrection data (target distance and control input vector) based on thestate vector of the target input from the external device. The trackingprocessor 2 d uses, for track calculation, a predicted state and afilter gain (Kalman gain matrix) indirectly calculated from thecorrection data (target distance and control input vector) from thecorrection data calculation unit 5 d for each of the plurality of motionmodels. It is therefore possible to reduce the track error (randomcomponent and bias component) of a target that performs non-maneuver andthe track error (bias component) of a target that performs maneuver.

Fifth Embodiment

In a target tracking device according to the fifth embodiment, thefunctions of the correction data calculation unit 5 a and the trackingprocessor 2 a shown in FIG. 1 are slightly different from those of thefirst embodiment. In the fifth embodiment, reference numeral 5 e denotesa correction data calculation unit; and 2 e, a tracking processor. Onlyparts different from the target tracking device according to the firstembodiment will be explained below.

The correction data calculation unit 5 e calculates correction data on alocal coordinate system (polar coordinate system) about a passive sensor1 based on the state vector of a target sent via a communication unit 4.In the fifth embodiment, the correction data calculation unit 5 ecalculates the target distance (distance from the passive sensor 1 tothe target), the process noise covariance matrix of the target, and thecontrol input vector as the correction data. The correction data is sentto the tracking processor 2 e.

Based on angular measurement from the passive sensor 1 and thecorrection data (target distance, process noise covariance matrix, andcontrol input vector) from the correction data calculation unit 5 e, thetracking processor 2 e calculates predicted state for each of aplurality of motion models and updated state for each of the pluralityof motion models. The tracking processor 2 e also calculates an updatedstate by weighted sum of the updated states for the plurality of motionmodels, and sends the updated state to a control unit 3 as the targettrack. The functions of the target tracking device according to thefifth embodiment will be described next.

FIG. 6 is a flowchart illustrating the procedure executed by the targettracking device according to the fifth embodiment. Note that onlyprocessing procedures different from those of the first to fourthembodiments will be explained below.

When tracking processing starts, angular measurement is input (ST101).That is, the passive sensor 1 measures the target based on a controlsignal from the control unit 3, and sends the angular measurement of thetarget to the tracking processor 2 e. The tracking processor 2 eacquires the angular measurement sent from the passive sensor 1.

Correction data calculation processing (target distance) is executed(ST111). That is, the correction data calculation unit 5 e calculatesdistance data from the passive sensor 1 to the target based on the statevector of the target input from the external device. The correction datacalculation unit 5 e sends the distance data to the tracking processor 2e as the correction data (target distance).

Correction data calculation processing (covariance) is executed (ST121).That is, the correction data calculation unit 5 e calculates the processnoise covariance matrix of the target based on the state vector of thetarget input from the external device. The process noise covariancematrix is sent to the tracking processor 2 e as the correction data(process noise covariance matrix). Note that as the external device, forexample, a radar device including an adaptive Kalman filter or an IMMfilter capable of estimating the process noise covariance matrix of thetarget is usable.

Correction data calculation processing (control input) is executed(ST131). That is, the correction data calculation unit 5 e calculatesthe control input vector of the target based on the state vector of thetarget input from the external device. The control input vector is sentto the tracking processor 2 e as the correction data (control inputvector).

Covariance calculation processing is executed (ST112). That is, thetracking processor 2 e calculates a process noise covariance matrix foreach of the plurality of motion models based on the correction data(target distance) from the correction data calculation unit 5 e.

Motion model probability calculation processing is then executed(ST122). That is, the tracking processor 2 e calculates a motion modelprobability for each of the plurality of motion models based on thecorrection data (process noise covariance matrix) from the correctiondata calculation unit 5 e.

Prediction processing is executed (ST132). That is, the trackingprocessor 2 e calculates the predicted state of the target and itscovariance matrix for each of the plurality of motion models based onthe updated states of the target and their covariance matrices for theplurality of motion models and the correction data (target distance andcontrol input vector) from the correction data calculation unit 5 e. Theupdated states of the target and their covariance matrices for theplurality of motion models are calculated in step ST114 of the precedingmeasurement.

Update processing is then executed (ST114). That is, based on theangular measurement of the target from the passive sensor 1 and thepredicted states of the target and their covariance matrices for theplurality of motion models, the tracking processor 2 e calculates theupdated state of the target and its covariance matrix for each of theplurality of motion models. The tracking processor 2 e also calculatesthe updated state of the target and its covariance matrix by weightedsum of the updated states and their covariance matrix. The updated stateof the target and its covariance matrix are sent to the control unit 3as the target track.

Control processing is executed (ST104). That is, based on the targettrack from the tracking processor 2 e, the control unit 3 generates acontrol signal to control the posture and the like of the passive sensor1. The control signal is sent to the passive sensor 1. The processing ofsteps ST101 to ST105 is continued until the end.

The processing contents of the correction data calculation unit 5 e andthe tracking processor 2 e will be described next in detail. The motionmodel of the target is the same as in the third embodiment. Themeasurement model of the passive sensor 1 is the same as in the firstembodiment. The processing contents of steps ST101, ST111, ST112, ST104,and ST105 are the same as in the first embodiment. The processingcontents of steps ST121 and ST122 are the same as in the secondembodiment. The processing contents of steps ST131 and ST132 are thesame as in the third embodiment.

In step ST114, the tracking processor 2 e executes update processingusing a motion model probability p^(i) _(k−1) corresponding to the ithmotion model, which is calculated by equation (40) in step ST122 inplace of equation (34) of update processing of the preceding measurement(step ST114).

As described above, according to the target tracking device of the fifthembodiment, the correction data calculation unit 5 e calculatescorrection data (target distance, process noise covariance matrix, andcontrol input vector) based on the state vector of the target input fromthe external device, and sends the correction data to the trackingprocessor 2 e. The tracking processor 2 e uses, for track calculation, apredicted state and a filter gain (Kalman gain matrix) indirectlycalculated from the correction data (target distance and control inputvector) from the correction data calculation unit 5 e for each of theplurality of motion models. This makes it possible to reduce the trackerror (random component and bias component) of a target that performsnon-maneuver and the track error (bias component) of a target thatperforms maneuver.

Additionally, the tracking processor 2 e uses, for track calculation,the motion model probability indirectly calculated from the correctiondata (process noise covariance matrix) for each of the plurality ofmotion models. This allows to reduce the track error for both a targetthat performs non-maneuver and a target that performs maneuver atinitial tracking stage when the number of times of measurement is small.Also, this allows to reduce the track error for both the target thatperforms non-maneuver and the target that performs maneuver even whenthe motion model of the target has changed after the precedingmeasurement.

Modified Embodiment

The target tracking devices according to the first to fifth embodimentscan be modified as shown in FIGS. 7A, 7B, and 7C. A target trackingdevice shown in FIG. 7A comprises communication units 6 a and 6 bbetween the correction data calculation units 5 a to 5 e and thetracking processors 2 a to 2 e. The block including the correction datacalculation units 5 a to 5 e and the block including the trackingprocessors 2 a to 2 e are thus separated.

A target tracking device shown in FIG. 7B comprises communication units6 c and 6 d between the passive sensor 1 and the tracking processors 2 ato 2 e. The block including the passive sensor 1 and the block includingthe tracking processors 2 a to 2 e are thus separated.

A target tracking device shown in FIG. 1C comprises communication units6 a and 6 bc between the correction data calculation units 5 a to 5 eand the tracking processors 2 a to 2 e. In addition, the target trackingdevice comprises the communication unit 6 d between the communicationunit 6 bc and the passive sensor 1. The block including the correctiondata calculation units 5 a to 5 e, the block including the passivesensor 1, and the block including the tracking processors 2 a to 2 e arethus separated.

In any of the modifications, the same effects as described in the firstto fifth embodiments can be obtained.

As described above, according to each of the above-describedembodiments, it is possible to obtain a target tracking device havingimproved tracking performance for both a target that performsnon-maneuver and a target that performs maneuver.

Another Embodiment

FIG. 10 is a block diagram showing an example of a target trackingdevice according to another embodiment. The target tracking device shownin FIG. 10 comprises a plurality of systems of target tracking deviceseach including a passive sensor. That is, the target tracking deviceshown in FIG. 10 uses a passive sensor as the external device describedin the first to fifth embodiments.

One processing system including one passive sensor calculates one targettrack. Using two processing systems allows to calculate two targettracks that are independent of each other. As is known, the targetdistance can be calculated based on the principle of triangulation usingthe pair of target tracks. Another embodiment utilizes this fact. Thetechnique of this type is sometimes called “stereo view”.

The target tracking device shown in FIG. 10 comprises a main system, asubsystem, a distance calculation unit 7, and a correction datacalculation unit 5 a. The distance calculation unit 7 calculatesdistance data from target tracks individually calculated by the mainsystem and the subsystem. The correction data calculation unit 5 acalculates correction data based on the data given by the distancecalculation unit 7.

The main system comprises a passive sensor 1-1, a tracking processor 2a-1, and a control unit 3-1. The subsystem comprises a passive sensor1-2, a tracking processor 2 a-2, and a control unit 3-2. The main systemand the subsystem are connected via communication units 4-1 and 4-2. Thetarget track calculated by the tracking processor 2 a-2 of the subsystemis sent to the distance calculation unit 7 via the communication units4-1 and 4-2. The target track calculated by the tracking processor 2 a-1of the main system is also sent to the distance calculation unit 7.

The distance calculation unit 7 calculates distance data to the targetunder the principle of triangulation based on the target trackcalculated by the tracking processor 2 a-1 of the main system and thatcalculated by the tracking processor 2 a-2 of the subsystem. Thedistance calculation unit 7 also calculates state vector of the targetbased on the target track calculated by the tracking processor 2 a-1 andthe distance data to the target. The state vector is sent to thecorrection data calculation unit 5 a and used to calculate correctiondata. When calculating correction data based on the state vector, forexample, the method described in the first embodiment is usable.

According to another embodiment, it is possible to calculate the targetdistance without using an external device for directly measuring thetarget distance. Hence, the target tracking device of another embodimentis especially suitably applicable to consumer appliances.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. A target tracking device comprising: a trackcalculation unit configured to calculate a track of a target based onangular measurement of the target measured by a passive sensor; and acorrection unit configured to send correction data to the trackcalculation unit, wherein the correction unit calculates the correctiondata based on a state vector of the target input from an externaldevice, and the track calculation unit calculates track for each of aplurality of motion models based on the angular measurement and thecorrection data, and calculates the track of the target based on thetrack for each of the motion models, and calculates the track of thetarget by weighted sum of all tracks for the motion models.
 2. Thetarget tracking device of claim 1, wherein the correction unitcalculates distance data from the passive sensor to the target based onthe state vector of the target, and sends the distance data to the trackcalculation unit, and the track calculation unit calculates a processnoise covariance matrix for each of the motion models based on thedistance data, and calculates the track for each of the motion modelsbased on the process noise covariance matrix for each of the motionmodels.
 3. The target tracking device of claim 1, wherein the correctionunit calculates distance data from the passive sensor to the targetbased on the state vector of the target including a process noisecovariance matrix, calculates process noise covariance matrix of thetarget at a position of the passive sensor based on the state vector ofthe target including the process noise covariance matrix, and sends thedistance data and the process noise covariance matrix to the trackcalculation unit, and the track calculation unit calculates a processnoise covariance matrix and a motion model probability for each of themotion models based on the distance data and the process noisecovariance matrix, and calculates the track of the target based on theprocess noise covariance matrix and the motion model probability foreach of the motion models.
 4. The target tracking device of claim 1,wherein the correction unit calculates control input vector of thetarget at a position of the passive sensor based on the state vector ofthe target, and sends the control input vector to the track calculationunit, and the track calculation unit calculates a predicted state of thetarget for each of the motion models based on the control input vector,and calculates the track of the target based on the predicted state foreach of the motion models.
 5. The target tracking device of claim 1,wherein the correction unit calculates control input vector of thetarget at a position of the passive sensor based on the state vector ofthe target, and calculates distance data from the passive sensor to thetarget based on the state vector of the target, and the trackcalculation unit calculates a predicted state of the target for each ofthe motion models based on the control input vector, calculates aprocess noise covariance matrix of the target for each of the motionmodels based on the distance data, and calculates the track of thetarget based on the predicted state and the process noise covariancematrix for each of the motion models.
 6. The target tracking device ofclaim 1, wherein the correction unit calculates control input vector ofthe target at a position of the passive sensor based on the state vectorof the target including a process noise covariance matrix, calculatesdistance data from the passive sensor to the target based on the statevector of the target including the process noise covariance matrix, andcalculates process noise covariance matrix of the target at the positionof the passive sensor based on the state vector of the target includingthe process noise covariance matrix, and the track calculation unitcalculates a predicted state of the target for each of the motion modelsbased on the control input vector of the target, calculates a processnoise covariance matrix and a motion model probability of the target foreach of the motion models based on the distance data and the processnoise covariance matrix, and calculates the track of the target based onthe predicted state, the process noise covariance matrix, and the motionmodel probability for each of the motion models.
 7. A target trackingmethod applied to a target tracking device, wherein the device includes:a track calculation unit configured to calculate a track of a targetbased on angular measurement of the target measured by a passive sensor,and a correction unit configured to send correction data to the trackcalculation unit, wherein the method comprising: calculating, by thecorrection unit, the correction data based on a state vector of thetarget input from an external device, calculating, by the trackcalculation unit, track for each of a plurality of motion models basedon the angular measurement and the correction data, calculating, by thetrack calculation unit, the track of the target based on the track foreach of the motion models, and calculating, by the track calculationunit, the track of the target by weighted sum of all tracks for themotion models.
 8. The target tracking method of claim 7, wherein thecalculating the correction data includes calculating distance data fromthe passive sensor to the target based on the state vector of thetarget, the calculating the correction data includes sending thedistance data to the track calculation unit, the calculating the trackincludes calculating a process noise covariance matrix for each of themotion models based on the distance data, and the calculating the trackincludes calculating the track for each of the motion models based onthe process noise covariance matrix for each of the motion models. 9.The target tracking method of claim 7, wherein the calculating thecorrection data includes calculating distance data from the passivesensor to the target based on the state vector of the target including aprocess noise covariance matrix, the calculating the correction dataincludes calculating process noise covariance matrix of the target at aposition of the passive sensor based on the state vector of the targetincluding the process noise covariance matrix, the calculating thecorrection data includes sending the distance data and the process noisecovariance matrix to the track calculation unit, the calculating thetrack includes calculating a process noise covariance matrix and amotion model probability for each of the motion models based on thedistance data and the process noise covariance matrix, and thecalculating the track includes calculating the track of the target basedon the process noise covariance matrix and the motion model probabilityfor each of the motion models.
 10. The target tracking method of claim7, wherein the calculating the correction data includes calculatingcontrol input vector of the target at a position of the passive sensorbased on the state vector of the target, the calculating the correctiondata includes sending the control input vector to the track calculationunit, the calculating the track includes calculating a predicted stateof the target for each of the motion models based on the control inputvector, and the calculating the track includes calculating the track ofthe target based on the predicted state for each of the motion models.11. The target tracking method of claim 7, wherein the calculating thecorrection data includes calculating control input vector of the targetat a position of the passive sensor based on the state vector of thetarget, the calculating the correction data includes calculatingdistance data from the passive sensor to the target based on the statevector of the target, the calculating the track includes calculating apredicted state of the target for each of the motion models based on thecontrol input vector, the calculating the track includes calculating aprocess noise covariance matrix of the target for each of the motionmodels based on the distance data, and the calculating the trackincludes calculating the track of the target based on the predictedstate and the process noise covariance matrix for each of the motionmodels.
 12. The target tracking method of claim 7, wherein thecalculating the correction data includes calculating control inputvector of the target at a position of the passive sensor based on thestate vector of the target including a process noise covariance matrix,the calculating the correction data includes calculating distance datafrom the passive sensor to the target based on the state vector of thetarget including the process noise covariance matrix, the calculatingthe correction data includes calculating process noise covariance matrixof the target at the position of the passive sensor based on the statevector of the target including the process noise covariance matrix, thecalculating the track includes calculating a predicted state of thetarget for each of the motion models based on the control input vectorof the target, the calculating the track includes calculating a processnoise covariance matrix and a motion model probability of the target foreach of the motion models based on the distance data and the processnoise covariance matrix, and the calculating the track includescalculating the track of the target based on the predicted state, theprocess noise covariance matrix, and the motion model probability foreach of the motion models.
 13. A target tracking device comprising: afirst passive sensor; a first track calculation unit configured tocalculate a track of a target based on angular measurement of the targetmeasured by the first passive sensor; a correction unit configured tosend correction data to the first track calculation unit; a secondpassive sensor; a second track calculation unit configured to calculatethe track of the target based on angular measurement of the targetmeasured by the second passive sensor; and a distance calculation unitconfigured to calculate distance data of the target based on the trackof the target calculated by the first track calculation unit and thetrack of the target calculated by the second track calculation unit, andcalculate state vector of the target based on the track of the targetcalculated by the first track calculation unit and the distance data ofthe target, wherein the correction unit calculates the correction databased on the state vector, and the first track calculation unitcalculates track for each of a plurality of motion models based on theangular measurement measured by the first passive sensor and thecorrection data, and calculates the track of the target based on thetrack for each of the motion models.
 14. A target tracking methodapplied to a target tracking device, wherein the device includes: afirst passive sensor; a first track calculation unit configured tocalculate a track of a target based on angular measurement of the targetmeasured by the first passive sensor; a correction unit configured tosend correction data to the first track calculation unit; a secondpassive sensor; a second track calculation unit configured to calculatethe track of the target based on angular measurement of the targetmeasured by the second passive sensor; and a distance calculation unitconfigured to calculate distance data of the target based on the trackof the target calculated by the first track calculation unit and thetrack of the target calculated by the second track calculation unit, andcalculate state vector of the target based on the track of the targetcalculated by the first track calculation unit and the distance data ofthe target, wherein the method comprising: calculating, by thecorrection unit, the correction data based on the state vector,calculating, by the first track calculation unit, track for each of aplurality of motion models based on the angular measurement measured bythe first passive sensor and the correction data, and calculating, bythe first track calculation unit, the track of the target based on thetrack for each of the motion models.